Modeling the performance of batch ultrafiltration of synthetic fruit juice and mosambi juice using artificial neural network

Rai, P.; Majumdar, Gautam; DasGupta, Sunando; De, Sirshendu

doi:10.1016/j.jfoodeng.2005.02.003

Cited by 43 publications

(12 citation statements)

References 27 publications

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“…Rai and others [30] used ANN to describe the permeate flux and permeate concentration (total soluble solid) profiles during the ultrafiltration of synthetic fruit juice and mosambi (sweet lime) juice dynamically. They predicted the permeate flux and total soluble solid of permeate as a function of transmembrane pressure, sucrose, pectin concentration in the feed and the processing time.…”

Section: U Mosambi (Sweet Lime) Juicementioning

confidence: 99%

Artificial Neural Networks in Fruits: A Comprehensive Review

Goyal¹

2014

IJIGSP

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Abstract-This review discusses the application of artificial neural networks (ANN) modeling in fruits. It covers all fruits in which ANN modeling has been applied. ANN is quite a new and easy computational modeling approach used for prediction, which has become popular and accepted by food industry, researchers, scientists and students. ANNs have been applied in almost every field of science and technology, viz., speech synthesis & recognition, pattern classification, adaptive interfaces between humans & complex physical systems, clustering, function approximation, image data compression, non-linear system modeling, associative memory, combinatorial optimization, control and several others, as they have proved valuable tools for obtaining the required output. ANN provides an exciting alternative method for solving a variety of problems in different areas of science and engineering. The aim of this communication is to discover the recent advances of ANN technology implemented in fruits, and discuss the critical role that ANN plays in predictive modelling.

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Section: U Mosambi (Sweet Lime) Juicementioning

confidence: 99%

Artificial Neural Networks in Fruits: A Comprehensive Review

Goyal¹

2014

IJIGSP

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“…Furthermore, even quite simple network architectures can reproduce highly complex non-linear behaviour. Recently, ANNs have been employed in a number of studies to model the flux dynamics of crossflow membrane filtration processes, including microfiltration [22], [23], [24], [25], [26] and [27], but have not yet been employed for dead-end microfiltration of particulate or cellular suspensions.…”

Section: Introductionmentioning

confidence: 99%

Dead-end filtration of yeast suspensions: Correlating specific resistance and flux data using artificial neural networks

Mhurchú

Foley

2006

Journal of Membrane Science

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The specific cake resistance in dead-end filtration is a complex function of suspension properties and operating conditions. In this study, the specific resistance of resuspended dried bakers yeast suspensions was measured in a series of 150 experiments covering a range of pressures, cell concentrations, pHs, ionic strengths and membrane resistances. The specific resistance was found to increase linearly with pressure and exhibited a complex dependence on pH and ionic strength. The specific resistance data were correlated using an artificial neural network containing a single hidden layer with nine neurons employing the sigmoidal activation function. The network was trained with 104 training points, 13 validation points and 33 test points. Excellent agreement was obtained between the neural network and the test data with average errors of less than 10%. In addition, a network was trained for prediction of the filtrate flux directly from the system inputs and this approach is easily extended to crossflow filtration by adding inputs such as the crossflow velocity and channel height. An attempt was made to interpret the network weights for both the specific resistance and flux networks. The effective contribution of each input to the system output was computed in each case and showed trends that were as expected. Although network weights, and consequently the computed effect of each parameter, is different each time a network is changed (depending on the initial weights used in the training process), the variation was low enough for information contained in the network to be interpreted in a meaningful way.Keywords: Dead-end filtration; Specific cake resistance; Artificial neural network; Bakers yeast

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“…Dornier et al [20] 1995 MF FFNN a -MLP b SL c -BP d Modeling of cross-flow MF Niemi et al [21] 1995 RO FFNN-MLP SL-BP Simulation of RO membrane separation Bowen et al [22] 1998 UF FFNN-MLP SL-BP Prediction of the rate of cross-flow membrane UF of colloids Delgrange et al [23] 1998 UF FFNN-MLP SL-BP-QNLA e Prediction of UF transmembrane pressure in drinking water production Hamachi et al [24] 1999 MF RN f USL g Modeling of cross-flow MF of bentonite suspension Teodosiu et al [25] 2000 UF FFNN-MLP SL-BP Predicting the time dependence of flux evolution in UF Bowen et al [26] 2000 NF FFNN-MLP CGM h -SCG i Predicting salt rejections at NF Jafar and Zilouchian [27] 2001 RO FFNN-MLP and RBF j SL-BP Modeling of an RO water desalination Bhattacharjee and Singh [28] 2002 UF FFNN-MLP SL-BP Modeling of a continuous stirred UF process Razavi et al [29] 2003 UF FFNN-MLP SL-BP Prediction of milk UF performance Shetty et al [30] 2003 NF FFNN-MLP SL-BP-LMT k Prediction of contaminant removal and membrane fouling during NF Lee et al [31] 2004 FC FFNN-MLP SL-BP Modeling of polymer electrolyte membrane FC performance Aydiner et al [32] 2005 MF FFNN-MLP SL-BP Modeling of flux decline in cross-flow MF Zhao et al [33] 2005 RO and NF FFNN-MLP and NRBF l SL-BP Predicting RO/NF water quality Abbas and Al-Bastaki [34] 2005 RO FFNN-MLP SL-BP-LMT Modeling of an RO water desalination Rai et al [35] 2005 UF FFNN-MLP SL-BP Modeling batch UF of synthetic fruit juice and mosambi juice Geissler et al [36] 2005 MBR SRN m (EN n ) SL-BP Modeling of capillary modules in MBR Ou and Achenie [37] 2005 FC FFNN-MLP and RBF SL-BP Modeling of proton exchange membranes Chen and Kim [38] 2006 FT FFNN-MLP and RBF SL-BP-LMT Prediction of permeate flux decline in cross-flow membrane FT Cinar et al [39] 2006 MBR CFNN o -MLP SL-BP…”

Section: Referencementioning

confidence: 99%